A central challenge of human genetics is to determine which regions of the genome are of functional importance and, in particular, which subset influences the risk of disease. This research usually includes linkage analyses or association studies. A complementary, population-genetic based approach is to identify likely targets of adaptation in the human lineage by examining patterns of nucleotide variation within humans and between humans and their close evolutionary relatives. Genetic changes involved in the architecture of human-specific adaptations are of great interest in their own right and represent promising candidates for complex disease susceptibility. We propose to model positive selection in a variety of demographic settings that are plausible for humans. On this basis, we will develop robust statistical methods to assess the support in polymorphism data for a recent adaptation and to estimate its timing and genomic location. The reliability of the new methods will be tested on data simulated under a range of assumptions. User-friendly programs to implement them will be made publicly available via the web. We will apply our new methods to polymorphism data from genes with human-specific expression patterns that result from changes in the cis-regulatory regions. These genes will be identified from a set that our previous work has shown to be differentially expressed in the human brain relative to other apes. Once the expression patterns have been confirmed by RT-PCR, we will test by a reporter gene assay whether sequence differences between humans and chimpanzees in approximately 1.5kb region around the transcription start site affect expression. If they do, we will collect polymorphism data centered on the 5' region in 30 Yoruba individuals and apply our new methods to gauge the evidence for a recent adaptation. Thus, the proposed research will generate new statistical tools for the detection of regions of functional importance and will identify the first set of candidates for human-specific adaptations in gene expression regulation.